DocumentCode :
624277
Title :
Automating natural disaster impact analysis: An open resource to visually estimate a hurricane´s impact on the electric grid
Author :
Barker, Alan M. ; Freer, Eva B. ; Omitaomu, Olufemi A. ; Fernandez, Steven J. ; Chinthavali, Supriya ; Kodysh, Jeffrey B.
Author_Institution :
Oak Ridge Nat. Lab., Oak Ridge, TN, USA
fYear :
2013
fDate :
4-7 April 2013
Firstpage :
1
Lastpage :
3
Abstract :
An ORNL team working on the Energy Awareness and Resiliency Standardized Services (EARSS) project developed a fully automated procedure to take wind speed and location estimates provided by hurricane forecasters and provide a geospatial estimate on the impact to the electric grid in terms of outage areas and projected duration of outages. Hurricane Sandy was one of the worst US storms ever, with reported injuries and deaths, millions of people without power for several days, and billions of dollars in economic impact. Hurricane advisories were released for Sandy from October 22 through 31, 2012. The fact that the geoprocessing was automated was significant - there were 64 advisories for Sandy. Manual analysis typically takes about one hour for each advisory. During a storm event, advisories are released every two to three hours around the clock, and an analyst capable of performing the manual analysis has other tasks they would like to focus on. Initial predictions of a big impact and landfall usually occur three days in advance, so time is of the essence to prepare for utility repair. Automated processing developed at ORNL allowed this analysis to be completed and made publicly available within minutes of each new advisory being released.
Keywords :
power engineering computing; power grids; power system faults; storms; automated processing; automating natural disaster impact analysis; electric grid; energy awareness and resiliency standardized services project; geoprocessing; hurricane impact; Government; Hurricanes; Manuals; Real-time systems; Storms; Substations; Wind speed; EARSS; Electric Grid; Hurricane; Python;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Southeastcon, 2013 Proceedings of IEEE
Conference_Location :
Jacksonville, FL
ISSN :
1091-0050
Print_ISBN :
978-1-4799-0052-7
Type :
conf
DOI :
10.1109/SECON.2013.6567495
Filename :
6567495
Link To Document :
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